# Business Statistics and Quantitative Analysis - What is your Prediction?

*label*Mathematics

*timer*Asked: Dec 12th, 2018

*account_balance_wallet*$30

**Question description**

Topic: What is your Prediction?

A time series model is a forecasting technique that attempts to predict the future values of a variable by using only historical data on that one variable. Here are some examples of variables you can use to forecast. You may use a different source other than the ones listed (be sure to reference the website). There are many other variables you can use, as long as you have values that are recorded at successive intervals of time.

- Currency price: XE (http://www.xe.com/currencyconverter/)
- GNP: Trading Economics (http://www.tradingeconomics.com/united-states/gross-national-product)
- Average home sales: National Association of Realtors (http://www.realtor.org/topics/existing-home-sales)
- College tuition: National Center for Education Statistics (https://nces.ed.gov/fastfacts/display.asp?id=76)
- Weather temperature or precipitation: (http://www.weather.gov/help-past-weather)
- Stock price: Yahoo Finance (https://finance.yahoo.com)

Main Post: Once you have historical data, address the following:

1. State the variable you are forecasting.

2. Collect data for any time horizon (daily, monthly, yearly). Select at least 8 data values.

3. Use the Time Series Forecasting Templates to forecast using moving average, weighted moving average, and exponential smoothing.

4. Copy/paste the results of each method into your post. Be sure to state the number of periods used in the moving average method, the weights used in the weighted moving average, and the value of α used in exponential smoothing.

5. Clearly state the “next period” prediction for each method.

## Tutor Answer

Thank you so much

Running head: BUSINESS STATISTICS AND QUANTITATIVE ANALYSIS

BUSINESS STATISTICS AND QUANTITATIVE ANALYSIS:

Name:

Institution affiliation:

Date:

1

BUSINESS STATISTICS AND QUANTITATIVE ANALYSIS

1.

2

State the variable you are forecasting.

The variable that will be analyzed will be (not seasonally adjusted) Sales Price of

Existing Single-Family Homes in the US over a period of one year by month (OCT 2017- OCT

2018). The data was obtained from the National Association of realtors Website

(http://www.realtor.org/topics/existinghome-sales) and it was downloaded from the “SingleFamily Existing Home Sales and Prices” section.

2.

Collect data for any time horizon (daily, monthly, yearly). Select at least 8 data

values.

Data

year

month

US

2017

October

4,880,000

2017

November 5,050,000

2017

December 4,950,000

2018

January

4,760,000

2018

February

4,960,000

2018

March

4,990,000

2018

April

4,840,000

2018

May

4,790,000

2018

June

4,760,000

2018

July

4,750,000

2018

August

4,740,000

2018

September 4,580,000

2018

October

4,620,000

3. Use the Time Series Forecasting Templates to forecast using moving average,

weighted moving average, and exponential smoothing.

Three month moving average- forecast is 4672500.00

Number

of

Periods

Average

d

4

Analysis of Forecast Error

MAD

MSE

MAPE

BUSINESS STATISTICS AND QUANTITATIVE ANALYSIS

3

91666.6

6667

10062500000 1.9339

.00000

0%

Data

Period

Number

Data

Indicates

which cells

in column to

the right

need a

formula

Movin

g

averag

e

forecas

t

error

absolut

e value

of error

squared

error

percen

tage

error

1

488000

0

505000

0

495000

0

476000

0

496000

0

499000

0

484000

0

Formula

Needed -->

Formula

Needed -->

Formula

Needed -->

491000

0.00

493000

0.00

491500

0.00

50000.0

0

60000.0

0

75000.0

0

2500000000.

00

3600000000.

00

5625000000.

00

1.01%

8

479000

0

Formula

Needed -->

488750

0.00

97500.0

0

9506250000.

00

2.04%

9

476000

0

Formula

Needed -->

489500

0.00

135000.

00

18225000000 2.84%

.00

10

475000

0

...

*flag*Report DMCA

Brown University

1271 Tutors

California Institute of Technology

2131 Tutors

Carnegie Mellon University

982 Tutors

Columbia University

1256 Tutors

Dartmouth University

2113 Tutors

Emory University

2279 Tutors

Harvard University

599 Tutors

Massachusetts Institute of Technology

2319 Tutors

New York University

1645 Tutors

Notre Dam University

1911 Tutors

Oklahoma University

2122 Tutors

Pennsylvania State University

932 Tutors

Princeton University

1211 Tutors

Stanford University

983 Tutors

University of California

1282 Tutors

Oxford University

123 Tutors

Yale University

2325 Tutors